---
language:
- es
- de
- fr
- pt
- en
library_name: transformers
---
# Model Card for sar-i-65b

## Model Details

- **Model Name**: sar-i-65b
- **Version**: 1.2
- **Developed by**: BushAI

## Intended Use

- **Primary Use Cases**:
  - Text generation
  - Language modeling
  - Natural language understanding tasks
  - Research and development in NLP

- **Out-of-Scope Use Cases**:
  - Real-time critical applications
  - High-stakes decision-making systems
  - Use in contexts where the model's output could be harmful or misleading

## Factors

- **Relevant Factors**:
  - Model performance may vary across different languages and domains.
  - The model may generate biased or inappropriate content, especially in sensitive contexts.

- **Evaluation Factors**:
  - Performance on benchmark datasets
  - Human evaluation of generated text
  - Ethical considerations and potential biases

## Limitations

- **Known Limitations**:
  - The model may generate biased or inappropriate content.
  - The model may not perform well on low-resource languages or specialized domains.
  - The model may require significant computational resources for inference.

## Ethical Considerations

- **Potential for Harm**:
  - The model may generate harmful or biased content, especially in sensitive contexts.
  - The model should not be used in high-stakes decision-making systems.

- **Mitigations**:
  - Regularly evaluate the model for biases and ethical concerns.
  - Use the model in conjunction with human oversight.
  - Provide clear guidelines and warnings for users of the model.

## How to Get Started with the Model

- **Usage**:
  ```python
  from transformers import AutoTokenizer, AutoModelForCausalLM

  # Load the tokenizer and model
  tokenizer = AutoTokenizer.from_pretrained("bushai/sar-i-65b")
  model = AutoModelForCausalLM.from_pretrained("bushai/sar-i-65b")

  # Prepare the input text
  input_text = "Once upon a time"
  inputs = tokenizer(input_text, return_tensors="pt")

  # Generate text
  output = model.generate(**inputs, max_length=50)

  # Decode the output
  output_text = tokenizer.decode(output[0], skip_special_tokens=True)

  # Print the generated text
  print(output_text)```

- **Dependencies**:
  - transformers
  - torch